Analysis of cardiac left-ventricular volume based on time warping averaging

Caiani, Enrico G.; Porta, Alberto; Baselli, Giuseppe; Turiel, M.; Muzzupappa, S.; Pagani, Massimo; Malliani, Alberto; Cerutti, S.

doi:10.1007/bf02348129

Cited by 11 publications

(11 citation statements)

References 30 publications

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“…The DTW -based measure relies on finding the optimal (least cumulative) distance mapping a given time series into a reference time series, where both sequences may vary in time and/or speed. It was originally developed for speech recognition [45,46], but has been recently used for different data mining tasks in medicine and bioinformatics [43,47]. The concept of DTW is sketched in Figure 8 for two short time series with 4 time points each.…”

Section: Methodsmentioning

confidence: 99%

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

et al. 2011

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BackgroundInferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.ResultsHere we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.ConclusionsOur study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.

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Section: Methodsmentioning

confidence: 99%

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

et al. 2011

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“…A more detailed description of the computation of the TWF can be found elsewhere (Caiani et al 2002). Summarizing, given two BCG waveforms, S x (n) and S y (m), with different durations (1 ⩽ n ⩽ N and 1 ⩽ m ⩽ M, respectively) but equally sampled, the temporal correspondence between samples of S x and S y is defined by the monotonic and continuous TWF, matching the time axis n of the waveform S x in the time axis m of the waveform S y , and vice-versa.…”

Section: Calculation Of the Twfmentioning

confidence: 99%

“…To calculate the TWF, the following measure of dissimilarity (i.e. the local cost) between the normalised waveforms S x and S y was utilised, as described in Caiani et al (2002), taking into account their amplitude and first derivative differences:…”

Section: Calculation Of the Twfmentioning

confidence: 99%

Evaluation of respiratory- and postural-induced changes on the ballistocardiogram signal by time warping averaging

et al. 2017

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Objective: The aim of this work was to evaluate the potential changes in the ballistocardiogram (BCG) signal induced by different respiratory patterns and posture, by using the dynamic time warping (DTW) technique. Approach: BCG signals were recorded in a group of 20 healthy volunteers, simultaneously with an electrocardiogram (ECG). Two recordings, one in a supine (SUP) and one in a standing (ST) position, including spontaneous breathing, two 1 min apneas (at full and empty-lungs, respectively) and 30 s of Valsalva, were analyzed. A warped averaged waveform was computed for each phase, from which amplitude and temporal parameters were extracted to characterize each condition. Main results: Variations were found in both amplitude and duration of BCG-derived parameters among manoeuvres, especially when compared to spontaneous breathing, suggesting a complex interaction between intrathoracic pressure changes acting on venous return, together with the autonomic nervous system modulation on heart rate. The effect of a hydrostatic pressure gradient elicited by postural conditions was also evident. Significance: Posture and respiratory manoeuvres affect the BCG signal in different ways, probably as a result of changes induced in preload and afterload. This supports the need to

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“…For each of the three cardiac cycles analysed, end-diastolic and endsystolic areas (EDA and ESA, respectively), together with systolic decrease of area (EDA-ESA), were computed and then averaged to compensate for respiratory variations. Moreover, the LV area over time curve was obtained from the frame-by-frame analysis, fitted by a cubic spline interpolation and utilised to compute its first derivative, from which the following parameters were extracted (CAIANI et al, 2002): peak ejection (PER), peak filling (PFR) and peak atrial filling (PAFR) rate.…”

Section: Q U a N T I F I C A T I O N Of L V F U N C T I O N M O D I Fmentioning

confidence: 99%

Quantification of left ventricular modification in weightlessness conditions from the spatio-temporal analysis of 2D echocardiographic images

Corsi

Lamberti

Cerutti

et al. 2004

Med. Biol. Eng. Comput.

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Two-dimensional echocardiography (2DE) performed during flights with a parabolic trajectory to simulate weightlessness provides a unique means to study left ventricular (LV) modifications to prevent post-flight orthostatic intolerance in astronauts. However, conventional analysis of 2DE is based on manual tracings and depends on experience. Accordingly, the aim was objectively to quantify, from 2DE images, the LV modifications related to different gravity levels, by applying a semi-automated level-set border detection technique. The algorithm validation was performed by the comparison of manual tracing results, obtained by two independent observers with 20 images, with the semi-automated measurements. To quantify LV modifications, three consecutive cardiac cycles were analysed for each gravity phase (1 Gz, 1.8 Gz, 0 Gz). The level-set procedure was applied frame-by-frame to detect the LV endocardial contours and obtain LV area against time curves, from which end-diastolic (EDA) and end-systolic (ESA) areas were computed and averaged to compensate for respiratory variations. Linear regression (y = 0.91x + 1.47, r = 0.99, SEE:0.80cm2) and Bland-Altman analysis (bias = -0.58 cm2, 95% limits of agreement= +/- 2.14cm2) showed excellent correlation between the semi-automatic and manually traced values. Inter-observer variability was 5.4%, and the inter-technique variability was 4.1%. Modifications in LV dimensions during the parabola were found: compared with 1 Gz values, EDA and ESA were significantly reduced at 1.8 Gz by 8.8 +/- 5.5% and 12.1 +/- 10.1%, respectively, whereas, during 0 Gz, EDA and ESA increased by 13.3 +/- 7.3% and 11.6 +/- 5.1%, respectively, owing to abrupt changes in venous return. The proposed method resulted in fast and reliable estimations of LV dimensions, whose changes caused by different gravity conditions were objectively quantified.

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Analysis of cardiac left-ventricular volume based on time warping averaging

Cited by 11 publications

References 30 publications

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

Evaluation of respiratory- and postural-induced changes on the ballistocardiogram signal by time warping averaging

Quantification of left ventricular modification in weightlessness conditions from the spatio-temporal analysis of 2D echocardiographic images

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